Kalcsics / Nickel Operations Research Proceedings 2007

Selected Papers of the Annual International Conference of the German Operations Research Society (GOR)
1. Auflage 2008
ISBN: 978-3-540-77903-2
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2007, 502 Seiten

Reihe: Operations Research Proceedings

The symposium Operations Research 2007 was held from September 5-7, 2007 at the Saarland University in Saarbru ¨cken. This international conference is at the same time the annual meeting of the German - erations Research Society (GOR). The transition in Germany (and many other countries in Europe) from a production orientation to a service society combined with a continuous demographic change generated a need for intensi?ed Op- ations Research activities in this area. On that account this conference has been devoted to the role of Operations Research in the service industry. The links to Operations Research are manifold and include many di?erent topics which are particularly emphasized in scienti?c sections of OR 2007. More than 420 participants from 30 countries made this event very international and successful. The program consisted of three p- nary,elevensemi-plenaryandmorethan300contributedpresentations, which had been organized in 18 sections. During the conference, the GOR Dissertation and Diploma Prizes were awarded. We congratulate all winners, especially Professor Wolfgang Domschke from the Da- stadt University of Technology, on receiving the GOR Scienti?c Prize Award.

Autoren/Hrsg.

Kalcsics, Jörg

Nickel, Stefan

Inhaltsverzeichnis

1;Preface;5
2;Committees;7
2.1;Program Committee;7
2.2;Local Organizing Committee;7
3;Scientific Sections and Section Chairs;9
4;Contents;11
5;Part I Dissertation Award Winners;21
5.1;Expected Additive Time-Separable Utility Maximizing Capacity Control in Revenue Management;22
5.1.1;1 The Static Capacity Control Model;23
5.1.2;2 Maximizing Additive Time-Separable Expected Utility;25
5.1.3;3 Conclusion;27
5.1.4;References;27
5.2;Routing and Capacity Optimization for IP Networks;28
5.2.1;1 Metrics and Routing Paths;29
5.2.2;2 Hardness and Approximability;30
5.2.3;3 Solution Approaches;32
5.2.4;References;34
5.3;Coping with Incomplete Information in Scheduling – Stochastic and Online Models.;36
5.3.1;1 Stochastic Scheduling;37
5.3.2;2 Online Scheduling;39
5.3.3;3 Stochastic Online Scheduling;39
5.3.4;4 Conclusion;40
5.3.5;References;41
5.4;Availability and Performance Analysis of Stochastic Networks with Unreliable Nodes;42
5.4.1;1 Introduction;42
5.4.2;2 Degradable Exponential Networks of Product Form;43
5.4.3;3 Generalizations and Complements;47
5.4.4;References;47
6;Part II Diploma Award Winners;48
6.1;Heuristics of the Branch- Cut- and-Price-Framework SCIP;50
6.1.1;1 Introduction;50
6.1.2;2 Rounding Heuristics;51
6.1.3;3 Diving Heuristics;51
6.1.4;4 Objective Diving Heuristics;52
6.1.5;5 LNS Heuristics;52
6.1.6;6 Computational Results;53
6.1.7;References;55
6.2;Forecasting Optimization Model of the U.S. Coal, Energy and Emission Markets;56
6.3;Optimal Control Strategies for Incoming Inspection;62
6.3.1;1 Introduction;62
6.3.2;2 A Measure for Supplier’s Quality;63
6.3.3;3 Clustering;63
6.3.4;4 Optimal Time-Period Between Two Inspections;64
6.3.5;5 Simulation Results;66
6.3.6;6 Conclusions;67
6.3.7;References;67
6.4;An Extensive Tabu Search Algorithm for Solving the Lot Streaming Problem in a Job Shop Environment;68
6.4.1;1 Introduction;68
6.4.2;2 The Tabu Search Implementation;69
6.4.3;3 Kol-Heuristic;70
6.4.4;4 Computational Results;71
6.4.5;References;73
7;Part III Applied Probability and Stochastic Programming;74
7.1;Optimizing Consumption and Investment: The Case of Partial Information;76
7.1.1;1 Introduction;76
7.1.2;2 The Basic Model;77
7.1.3;3 Consumption and Investment Processes;77
7.1.4;4 Optimization;78
7.1.5;5 Gaussian Dynamics (GD) for the Drift;79
7.1.6;6 A Hidden Markov Model (HMM) for the Drift;80
7.1.7;7 Numerical Example;80
7.1.8;References;81
7.2;Multistage Stochastic Programs via Stochastic Parametric Optimization;82
7.2.1;1 Introduction;82
7.2.2;2 Problem Analysis;84
7.2.3;3 Some Auxiliary Assertions;85
7.2.4;4 Stability and Approximation;86
7.2.5;References;87
7.3;Risk-Sensitive Average Optimality in Markov Decision Chains;88
7.3.1;1 Introduction and Notation;88
7.3.2;2 Risk-Sensitive Optimality and Nonnegative Matrices;89
7.3.3;3 Finding Optimal Solutions by Value Iterations;92
7.3.4;References;93
7.4;A Stochastic Programming Model with Decision Dependent Uncertainty Realizations for Technology Portfolio Management;94
7.4.1;1 Introduction;94
7.4.2;2 Mathematical Representation and Model;95
7.4.3;3 An E.cient Solution Procedure;96
7.4.4;4 Conclusions;99
7.4.5;References;99
8;Part IV Artificial Intelligence, Business Intelligence and Decision Support;100
8.1;A Neural Network Based Decision Support System for Real- Time Scheduling of Flexible Manufacturing Systems;102
8.1.1;1 Introduction;102
8.1.2;2 Proposed Scheduler;103
8.1.3;3 The FMS Model;104
8.1.4;4 Experimental Results;105
8.1.5;5 Conclusions and Future Research;106
8.1.6;References;107
8.2;Improving Classi.er Performance by Using Fictitious Training Data? A Case Study;108
8.2.1;1 Introduction;108
8.2.2;2 Simple Effects of Added Fictitious Training Examples;109
8.2.3;3 Fictitious Points for Different Kernels on Real Data;111
8.2.4;4 Conclusions and Outlook;112
8.2.5;References;113
9;Part V Continuous Optimization;114
9.1;Artificial DMUs and Contingent Weight Restrictions for the Analysis of Brazilian Retail Banks Efficiency;116
9.1.1;1 Introduction;116
9.1.2;2 Description of Variables and Methodology;117
9.1.3;3 Analysis of Results and Comparisons;120
9.1.4;4 Conclusion;121
9.1.5;References;121
9.2;Performance of Some Approximate Subgradient Methods over Nonlinearly Constrained Networks.;122
9.2.1;1 Introduction;122
9.2.2;2 Calculation of the Stepsizes;123
9.2.3;3 Solution to NCNFP;124
9.2.4;4 Numerical Tests;125
9.2.5;References;127
10;Part VI Discrete and Combinatorial Optimization;128
10.1;Shortest-Path Algorithms and Dynamic Cost Changes;130
10.1.1;1 Introduction;130
10.1.2;2 Shortest-Path Algorithms in Dynamic Domains;130
10.1.3;3 Summary and Outlook;134
10.1.4;References;135
10.2;Solving Railway Track Allocation Problems.;136
10.2.1;1 Introduction;136
10.2.2;2 The Optimal Track Allocation Problem;137
10.2.3;3 Column Generation;138
10.2.4;4 Computational Results;140
10.2.5;References;141
10.3;On a Class of Interval Data Minmax Regret CO Problems;142
10.3.1;1 Introduction;142
10.3.2;2 Problem Formulation and Algorithm;143
10.3.3;3 Remarks on Algorithm;145
10.3.4;References;147
10.4;A Benders Decomposition for Hub Location Problems Arising in Public Transport;148
10.4.1;1 Introduction;148
10.4.2;2 Mathematical Formulation;149
10.4.3;3 Benders Decomposition Method for the HLPPT;150
10.4.4;4 Computational Results;152
10.4.5;5 Conclusions;152
10.4.6;References;153
10.5;Reliability Models for the Uncapacitated Facility Location Problem with User Preferences;154
10.5.1;1 Introduction;154
10.5.2;2 General Problem Formulation;155
10.5.3;3 Experimental Results;158
10.5.4;4 Conclusions;159
10.5.5;Acknowledgments;159
10.5.6;References;159
10.6;The Real-Time Vehicle Routing Problem;160
10.6.1;1 Introduction;160
10.6.2;2 Problem Description;160
10.6.3;3 Solution Algorithm;161
10.6.4;4 Computational Results;163
10.6.5;5 Conclusions;165
10.6.6;References;165
10.7;A Decision Support System for Planning Promotion Time Slots.;166
10.7.1;1 Introduction;166
10.7.2;2 Problem Description and Formulation;168
10.7.3;3 Methodology;169
10.7.4;4 Conclusions;171
10.7.5;References;171
10.8;Greedy Heuristics and Weight-Coded EAs for Multidimensional Knapsack Problems and Multi- Unit Combinatorial Auctions;172
10.8.1;1 Introduction;172
10.8.2;2 Heuristic Optimization Approaches;173
10.8.3;3 Experiments;175
10.8.4;4 Conclusions;177
10.8.5;References;177
10.9;A Metaheuristic for the Periodic Location- Routing Problem;178
10.9.1;1 Introduction;178
10.9.2;2 Iterative Metaheuristic;179
10.9.3;3 Computational Study;181
10.9.4;4 Conclusion;182
10.9.5;References;183
10.10;A New Formulation of the Capacitated Discrete Ordered Median Problems with {0, 1}-Assignment;184
10.10.1;1 Introduction;184
10.10.2;2 The New Formulation and First Properties;185
10.10.3;3 Computational Results;187
10.10.4;References;189
11;Part VII Econometrics, Game Theory and Mathematical Economics;190
11.1;Investment Timing Problem Under Tax Allowances: The Case of Special Economic Zones;192
11.1.1;1 Investment Waiting Model in Special Economic Zones;193
11.1.2;2 Optimal Investment Time and Present Tax Revenues;195
11.1.3;3 Budgetary Effects of the Creation of New Enterprises: The Example of Russian SEZ;196
11.1.4;References;197
11.2;Computing the Value of Information in Quadratic Stochastic Decision Problems;198
11.2.1;1 Introduction;198
11.2.2;2 An Example of Negative Information Value;199
11.2.3;3 The Generalized Quadratic Stochastic Game;201
11.2.4;4 Non-negativity Conditions of Information Value;202
11.2.5;References;203
11.3;How Often Are You Decisive: an Enquiry About the Pivotality of Voting Rules;204
11.3.1;1 Introduction;204
11.3.2;2 The Model;205
11.3.3;3 Applications;207
11.3.4;References;209
12;Part VIII Energy, Environment and Life Sciences;210
12.1;A System Analysis on PEFC-CGS for a Farm Household;212
12.1.1;1 Introduction;213
12.1.2;2 Peformance of PEFC-CGS;213
12.1.3;3 Energy Demand of a Farm House with a Greenhouse;214
12.1.4;4 System Analysis;214
12.1.5;5 Conclusions;216
12.1.6;References;216
12.2;Taming Wind Energy with Battery Storage;218
12.2.1;1 Background;218
12.2.2;2 Estimating Battery Capacity;220
12.2.3;3 On-line Heuristics;221
12.2.4;4 Results;221
12.2.5;5 Conclusions;223
12.2.6;References;223
12.3;The Inueflnce of Social Values in Cooperation;224
12.3.1;1 Introduction;224
12.3.2;2 Social Value Orientation;225
12.3.3;3 Experimental Design and Procedure;226
12.3.4;4 Hypotheses and Results;227
12.3.5;5 Summary;228
12.3.6;References;229
12.4;Designing Sustainable Supply Chains by Integrating Logistical and Process Engineering Aspects – A Material Flow Based Approach for 2nd Generation Synthetic Bio- Fuels;230
12.4.1;1 Introduction;230
12.4.2;2 Network Planning for 2nd Generation Bio-Fuels;231
12.4.3;3 Planning Concept;232
12.4.4;4 Conclusions;235
12.4.5;References;235
13;Part IX Entrepreneurship and Innovation;236
13.1;About the Limitations of Spreadsheet Applications in Business Venturing;238
13.1.1;1 The Mirage of Spreadsheet Applications;238
13.1.2;2 Beyond Conventional Spreadsheet Applications: An Illustrative Example of an Influence Diagram Model;239
13.1.3;3 Implications;241
13.1.4;4 Conclusion and Discussion;241
13.1.5;References;242
13.2;A Decision-Analytic Approach to Blue-Ocean Strategy Development;244
13.2.1;1 Introduction;244
13.2.2;2 The Strategy Canvas: A Qualitative Tool for Ex-post Strategy Diagnosis;245
13.2.3;3 The Strategy Canvas: A Quantitative Tool for Ex-ante Strategy Development;247
13.2.4;4 Conclusion;248
13.2.5;References;248
13.3;Flexible Planning in an Incomplete Market;250
13.3.1;1 Introduction;250
13.3.2;2 Rigid and Flexible Planning;250
13.3.3;3 Valuation in an Incomplete Market;253
13.3.4;References;254
13.4;Social Entrepreneurs, Lead Donors and the Optimal Level of Fundraising;256
13.4.1;1 Introduction;256
13.4.2;2 The Model;256
13.4.3;3 Donor Restrictions on Fundraising Expenditures;258
13.4.4;4 Conclusion;259
13.4.5;References;260
14;Part X Finance, Banking and Insurance;262
14.1;Studying Impact of Decision Making Units Features on Efficiency by Integration of Data Envelopment Analysis and Data Mining Tools;264
14.1.1;1 Introduction;264
14.1.2;2 An Integrated Algorithm for Decision Making Procedure;265
14.1.3;3 Case Study;265
14.1.4;4 Conclusion;269
14.1.5;References;269
14.2;Analysts’ Dividend Forecasts, Portfolio Selection, and Market Risk Premia;270
14.2.1;1 Introduction;270
14.2.2;2 Theoretical Background;270
14.2.3;3 Empirical Setting;271
14.2.4;4 Empirical Results;272
14.2.5;5 Conclusion;275
14.2.6;References;275
14.3;A Two-Stage Approach for Improving Service Management in Retail Banking;276
14.3.1;1 Introduction;276
14.3.2;2 Research;277
14.3.3;3 Conclusion;280
14.3.4;References;281
14.4;Non-maturing Deposits, Convexity and Timing Adjustments;282
14.4.1;1 Introduction;282
14.4.2;2 Deposits;282
14.4.3;3 Concluding Remarks;287
14.4.4;References;287
14.5;Nichtparametrische Prädiktorselektion im Asset Management;288
14.5.1;1 Einführung;288
14.5.2;2 Nichtparametrische Prädiktorselektion und Kernregressionsschätzer;289
14.5.3;3 Empirische Untersuchungen;291
14.5.4;4 Zusammenfassung;292
14.5.5;References;293
15;Part XI Forecasting and Marketing;294
15.1;Detecting and Debugging Erroneous Statements in Pairwise Comparison Matrices;296
15.1.1;1 Introduction;296
15.1.2;2 Errors in Preference Measurement;298
15.1.3;3 Identification of Erroneous Statements in Ratio Preference Networks;299
15.1.4;4 Simulation Study;300
15.1.5;5 Discussion and Conclusions;301
15.1.6;References;301
15.2;Prognose von Geldautomatenumsätzen mit SARIMAX- Modellen: Eine Fallstudie;302
15.2.1;1 Einleitung;302
15.2.2;2 Prognoseverfahren;303
15.2.3;3 Modellselektion;304
15.2.4;4 Ergebnisse und Prognosen;306
15.2.5;References;307
16;Part XII Health Care Management;308
16.1;On Dimensioning Intensive Care Units;310
16.1.1;1 Introduction;310
16.1.2;2 Original Model Formulation;311
16.1.3;3 A Modified OT-ICU System;312
16.1.4;4 Bounds;314
16.1.5;5 Application: Case Study;315
16.1.6;6 Conclusion;315
16.1.7;References;315
16.2;A Hybrid Approach to Solve the Periodic Home Health Care Problem;316
16.2.1;1 Introduction;316
16.2.2;2 A Model for Home Health Care Planning;317
16.2.3;3 Hybrid Approach;318
16.2.4;4 Computational Results;320
16.2.5;References;321
16.3;Tactical Operating Theatre Scheduling: Efficient Appointment Assignment;322
16.3.1;1 Introduction and Problem Description;322
16.3.2;2 Mathematical Formulation of the TOTSP;323
16.3.3;3 Solving the TOTSP;325
16.3.4;4 Computational Experience;326
16.3.5;5 Conclusions and Outlook;327
16.3.6;References;327
17;Part XIII Managerial Accounting and Auditing;328
17.1;Modeling and Analyzing the IAS 19 System of Accounting for Unfunded Pensions;330
17.1.1;1 General Research Question;330
17.1.2;2 A Brief Overview of the IAS 19 System;330
17.1.3;3 General Structure of the Simulation Model;332
17.1.4;4 Fundamental Results;334
17.1.5;5 Conclusions;335
17.1.6;References;335
17.2;Coordination of Decentralized Departments and the Implementation of a Firm- wide Differentiation Strategy;336
17.2.1;1 Introduction;336
17.2.2;2 Model;337
17.2.3;3 Performance Evaluation;339
17.2.4;References;341
17.3;Case-Based Decision Theory: An Experimental Report;342
17.3.1;1 Introduction;342
17.3.2;2 Funding Repetitive Decisions by Case-Based Decision Theory;342
17.3.3;3 An Experimental Study on Case-Based Decision Theory;345
17.3.4;4 Conclusion and Outlook;346
17.3.5;References;347
18;Part XIV Multi Criteria Decision Making;348
18.1;Truck Allocation Planning for Cost Reduction of Mechanical Sugarcane Harvesting in Thailand: An Application of Multi- objective Optimization;350
18.1.1;1 Introduction;350
18.1.2;2 Data Sources and Simulation;351
18.1.3;3 Application of MOO to Allocate Mechanized Resources;352
18.1.4;4 Computational Experiment;354
18.1.5;5 Results;354
18.1.6;6 Conclusions;355
18.1.7;References;355
18.2;Efficiency Measurement of Organizations in Multi- Stage Systems;356
18.2.1;1 Introduction;356
18.2.2;2 Global Efficient DMUs;358
18.2.3;3 Efficiency of Interdependent DMUs;359
18.2.4;4 Conclusion;360
18.2.5;References;361
19;Part XV Production and Service Operations Management;362
19.1;Construction Line Algorithms for the Connection Location- Allocation Problem;364
19.1.1;1 The Connection Location-Allocation Problem;364
19.1.2;2 The Construction Line Algorithm;366
19.1.3;3 Numerical Results and Conclusions;368
19.1.4;References;368
19.2;Service-Level Oriented Lot Sizing Under Stochastic Demand;370
19.2.1;1 The Model;370
19.2.2;2 Literature Review;371
19.2.3;3 Calculating The Service Level;371
19.2.4;4 Determining The Lot Sizes;372
19.2.5;5 Numerical Experiments;373
19.2.6;6 Observations and Insights;374
19.2.7;References;375
19.3;Real-Time Destination-Call Elevator Group Control on Embedded Microcontrollers;376
19.3.1;1 Introduction;376
19.3.2;2 Modeling the Destination Call System;377
19.3.3;3 Algorithms;378
19.3.4;4 Evaluation and Computational Results;379
19.3.5;References;381
19.4;Integrated Design of Industrial Product Service Systems;382
19.4.1;1 Introduction;382
19.4.2;2 Model Description;383
19.4.3;3 Comparison of the Two Business Models;384
19.4.4;4 Concluding Remarks;387
19.4.5;References;387
19.5;Lot Sizing Policies for Remanufacturing Systems;388
19.5.1;1 Introduction;388
19.5.2;2 Problem Setting and Model Formulation;389
19.5.3;3 Extension of the Model;391
19.5.4;4 Conclusion and Outlook;393
19.5.5;References;393
19.6;Multicriterial Design of Pharmaceutical Plants in Strategic Plant Management Using Methods of Computational Intelligence;394
19.6.1;1 Introduction;394
19.6.2;2 Structure of the Decision Support System;395
19.6.3;3 Case Example from the Pharmaceutical Industry;397
19.6.4;4 Summary and Prospects;398
19.6.5;References;399
20;Part XVI Retail, Revenue and Pricing Management;400
20.1;Optimizing Flight and Cruise Occupancy of a Cruise Line;402
20.1.1;1 Introduction;402
20.1.2;2 Revenue Management and Its Particularities in the Cruise Industry;402
20.1.3;3 Model Building;403
20.1.4;4 Optimization Results;406
20.1.5;5 Conclusion;407
20.1.6;References;407
20.2;Capacity Investment and Pricing Decisions in a Single- Period, Two- Product- Problem;408
20.2.1;1 Introduction;408
20.2.2;2 Model;409
20.2.3;3 Numerical Example;412
20.2.4;4 Conclusion;413
20.2.5;References;413
21;Part XVII Scheduling and Project Management;414
21.1;Relational Construction of Specific Timetables;416
21.1.1;1 Introduction;416
21.1.2;2 Relation-Algebraic Preliminaries;416
21.1.3;3 Informal Problem Description;418
21.1.4;4 Relation-Algebraic Model and Algorithmic Solution;418
21.1.5;5 Implementation and Results;420
21.1.6;References;421
21.2;Alternative IP Models for Sport Leagues Scheduling;422
21.2.1;1 Introduction;422
21.2.2;2 Models;422
21.2.3;3 Computational Results;425
21.2.4;References;427
21.3;Penalising Patterns in Timetables: Novel Integer Programming Formulations;428
21.3.1;References;433
21.4;Online Optimization of a Color Sorting Assembly Buffer Using Ant Colony Optimization;434
21.4.1;1 Introduction;434
21.4.2;2 Rule Based Approach for the CSRP;435
21.4.3;3 Supplementation of Storage Rules;436
21.4.4;4 ACO for CRP;437
21.4.5;5 ACO for CSP;438
21.4.6;6 Computational Results;438
21.4.7;7 Conclusions and Future Work;439
21.4.8;References;439
21.5;Scheduling of Tests on Vehicle Prototypes Using Constraint and Integer Programming;440
21.5.1;1 Introduction;440
21.5.2;2 Formal Problem Description;441
21.5.3;3 Complete CP Model;442
21.5.4;4 Simpli.ed IP Model;443
21.5.5;5 Computational Results;444
21.5.6;References;445
21.6;Complexity of Project Scheduling Problem with Nonrenewable Resources;446
21.6.1;1 Introduction;446
21.6.2;2 Problem De.nition;446
21.6.3;3 NP-hardness of the Project Scheduling Problem;448
21.6.4;References;450
22;Part XVIII Simulation, System Dynamics and Dynamic Modelling;452
22.1;Optimizing in Graphs with Expensive Computation of Edge Weights;454
22.1.1;1 Introduction;454
22.1.2;2 Algorithms;455
22.1.3;3 Applications to Molecular Transition Networks;457
22.1.4;4 Conclusions;459
22.1.5;References;459
22.2;Configuration of Order-Driven Planning Policies;460
22.2.1;1 Introduction;460
22.2.2;2 Conceptual Framework;461
22.2.3;3 Configuration of Order-Driven Planning;463
22.2.4;4 Conclusions;465
22.2.5;References;465
23;Part XIX Supply Chain Management and Traffic;466
23.1;When Periodic Timetables Are Suboptimal.;468
23.1.1;1 The Timetabling Problem;468
23.1.2;2 Periodic vs. Trip Timetables;470
23.1.3;3 Example;472
23.1.4;References;473
23.2;Acceleration of the A*-Algorithm for the Shortest Path Problem in Digital Road Maps;474
23.2.1;1 Speeding up the A*-Algorithm;474
23.2.2;2 Better Estimators for the A*-Algorithm;475
23.2.3;3 Using Segmentation Lines Without Preprocessing;477
23.2.4;4 Conclusion and Future Works;478
23.2.5;References;479
23.3;A Modulo Network Simplex Method for Solving Periodic Timetable Optimisation Problems;480
23.3.1;1 Introduction;480
23.3.2;2 The Periodic Timetable Polyhedron;482
23.3.3;3 Computational Results for a Real World Scenario;483
23.3.4;4 Acknowledgment;484
23.3.5;References;485
23.4;Simultaneous Vehicle and Crew Scheduling with Trip Shifting;486
23.4.1;1 Introduction;486
23.4.2;2 The Model;487
23.4.3;3 Extensions of the Model;489
23.4.4;4 Conclusions;491
23.4.5;References;491
23.5;Line Optimization in Public Transport Systems;492
23.5.1;1 Introduction;492
23.5.2;2 Model;493
23.5.3;3 Example;496
23.5.4;4 Conclusions;497
23.5.5;References;497
23.6;Coordination in Recycling Networks;498
23.6.1;1 Introduction;498
23.6.2;2 Coordination Levels in Recycling Networks;500
23.6.3;3 Conclusions and Outlook;503
23.6.4;References;503
23.7;Produktsegmentierung mit Fuzzy–Logik zur Bestimmung der Parameter eines Lagerhaltungsmodells für die Halbleiterindustrie;504
23.7.1;1 Einleitung;504
23.7.2;2 Fertigungs- und Produktstruktur der Infineon Technologies AG;505
23.7.3;3 Festlegung der Bevorratungsebene;505
23.7.4;4 Die Wahl der Bevorratungsebene;506
23.7.5;5 Zusammenfassung und Schlussfolgerungen;508
23.7.6;References;509
23.8;On the Value of Objective Function Adaptation in Online Optimisation;510
23.8.1;1 Introduction;510
23.8.2;2 Dynamic Decision Problem;511
23.8.3;3 Algorithm Details;512
23.8.4;4 Numerical Experiments;513
23.8.5;5 Conclusions and Outlook;515
23.8.6;References;515
23.9;A Novel Multi Criteria Decision Making Framework for Production Strategy Adoption Considering Interrelations;516
23.9.1;1 Introduction;516
23.9.2;2 Identifying Product, Firm and Process Considering External and Internal Environments;517
23.9.3;3 Application of Fuzzy ANP-SWOT Methodology;517
23.9.4;4 Illustrative Case Study;520
23.9.5;5 Conclusion;521
23.9.6;References;521